This thesis investigates whether neural networks can be used for option pricing while still respecting basic financial rules. It compares four models: two feed-forward neural networks and two physics-informed neural networks using synthetic option prices generated with the Heston model and the COS method. The models are evaluated not only by pricing accuracy, but also by no-arbitrage checks such as price bounds, monotonicity, convexity, maturity consistency, and put-call parity. The results show that the call-only FFNN gives the most accurate prices, while the hard-bounds PINN successfully removes price-bound violations. At the same time, convexity violations remain a challenge, indicating that accurate predictions do not automatically lead to financially consistent option surfaces.
Klíčová slova:
option pricing; feed-forward neural networks; COS method; physics-informed neural networks; no-arbitrage; Heston model
Název práce:
Applied Neural Network Methods for Option Pricing
Autor(ka) práce:
Boiko, Andre
Typ práce:
Diplomová práce
Vedoucí práce:
Čabla, Adam
Oponenti práce:
Danko, Jakub
Jazyk práce:
English
Abstrakt:
This thesis investigates whether neural networks can be used for option pricing while still respecting basic financial rules. It compares four models: two feed-forward neural networks and two physics-informed neural networks using synthetic option prices generated with the Heston model and the COS method. The models are evaluated not only by pricing accuracy, but also by no-arbitrage checks such as price bounds, monotonicity, convexity, maturity consistency, and put-call parity. The results show that the call-only FFNN gives the most accurate prices, while the hard-bounds PINN successfully removes price-bound violations. At the same time, convexity violations remain a challenge, indicating that accurate predictions do not automatically lead to financially consistent option surfaces.